UMAP
Brian M. Schilder
Most recent update:
2021-04-13
root.dir <- here::here()
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
root.dir = root.dir
# fig.height = 12,
# fig.width = 10
)
knitr::opts_knit$set(root.dir = root.dir, dpi = 300)
library(dplyr)
library(ggplot2)
library(Matrix)
set.seed(2020)
source("tfidf.R")
source("utils.R")
color_dict <- c(phenotype="purple",
celltype="turquoise")Human Phenotype Ontology
Data
HPO <- data.table::fread("https://ci.monarchinitiative.org/view/hpo/job/hpo.annotations/lastSuccessfulBuild/artifact/rare-diseases/util/annotation/phenotype_to_genes.txt", nThread = 10)
#> Warning in data.table::fread("https://ci.monarchinitiative.org/view/hpo/job/
#> hpo.annotations/lastSuccessfulBuild/artifact/rare-diseases/util/annotation/
#> phenotype_to_genes.txt", : Detected 1 column names but the data has 7 columns
#> (i.e. invalid file). Added 6 extra default column names at the end.
colnames(HPO) <- gsub("-|[ ]","_",stringr::str_split(gsub("#Format: ","",colnames(HPO)[1]),"<tab>")[[1]])
HPO <- HPO %>% dplyr::mutate(HPOid=HPO_id, HPO_id=gsub("[:]",".",HPO_id))Genes / term
gene_counts <- HPO %>%
dplyr::group_by(HPO_id, HPO_label) %>%
dplyr::summarise(genes=dplyr::n_distinct(entrez_gene_symbol)) %>%
dplyr::arrange(desc(genes))
#> `summarise()` has grouped output by 'HPO_id'. You can override using the `.groups` argument.
print(gene_counts)
#> # A tibble: 9,595 x 3
#> # Groups: HPO_id [9,595]
#> HPO_id HPO_label genes
#> <chr> <chr> <int>
#> 1 HP.0000707 Abnormality of the nervous system 3463
#> 2 HP.0033127 Abnormality of the musculoskeletal system 3369
#> 3 HP.0012638 Abnormal nervous system physiology 3239
#> 4 HP.0000152 Abnormality of head or neck 2878
#> 5 HP.0000234 Abnormality of the head 2855
#> 6 HP.0000924 Abnormality of the skeletal system 2835
#> 7 HP.0000478 Abnormality of the eye 2747
#> 8 HP.0011842 Abnormality of skeletal morphology 2726
#> 9 HP.0012639 Abnormal nervous system morphology 2688
#> 10 HP.0000007 Autosomal recessive inheritance 2658
#> # … with 9,585 more rows
dim(subset(gene_counts, genes==1))
#> [1] 1873 3
select_ids <- subset(gene_counts, genes>=4)$HPO_id
print(paste(length(select_ids),"/",n_distinct(gene_counts$HPO_id), "selected."))
#> [1] "6163 / 9595 selected."
hist(gene_counts$genes, breaks = 100) 
Create matrix
HPO$val <- 1
mat <- data.table::dcast.data.table(subset(HPO, HPO_id %in% select_ids),
formula = entrez_gene_symbol ~ HPO_id,
value.var = "val",
fill = 0,
fun.aggregate = max) %>%
data.frame() %>%
tibble::column_to_rownames(var = "entrez_gene_symbol") %>%
data.frame()
mat <- as(as.matrix(mat),"sparseMatrix")
## Specificity matrix
library(Matrix)
normalised_meanExp = t(t(mat)*(1/Matrix::colSums(mat)))
mat_spec = normalised_meanExp/(apply(normalised_meanExp,1,sum)+0.000000000001)Metadata
Ancestors
Use ontologyIndex to annotation terms further.
library(ontologyIndex)
data("hpo")
if(!exists("ancest_df")){
ancest_1 <- lapply(unique(HPO$HPOid), function(x){
# print(x)
tryCatch(expr = {
terms <- get_term_property(ontology=hpo, property="ancestors", term=x, as_names=T)
return(terms[2])
},
error = function(e)return(NULL))
}) %>% `names<-`(unique(HPO$HPOid))
ancest_2 <- lapply(unique(HPO$HPOid), function(x){
# print(x)
tryCatch(expr = {
terms <- get_term_property(ontology=hpo, property="ancestors", term=x, as_names=T)
return(terms[3])
},
error = function(e)return(NULL))
}) %>% `names<-`(unique(HPO$HPOid))
ancest_df <- rbind(data.frame(ancestor_label=unlist(ancest_1), ancestor_lvl=1),
data.frame(ancestor_label=unlist(ancest_2), ancestor_lvl=2)) %>%
tibble::rownames_to_column(var = "id") %>%
tidyr::separate(col = "id", sep = "[.]", into=c("HPOid", "ancestor_id")) %>%
data.table::data.table() %>%
data.table::melt.data.table(id.vars = c("HPOid","ancestor_lvl"),
measure.vars = c("ancestor_id","ancestor_label")) %>%
dplyr::mutate(variable=paste0(variable,ancestor_lvl)) %>%
data.table::dcast.data.table(formula = HPOid ~ variable, value.var = "value")
ancest_df
n_distinct(ancest_df$HPOid)
}
#> [1] 8383Merge metadata
Create metadata table for each term.
# parents <- data.table::fread("TermsToUse.csv")
meta <- unique(HPO[,c("HPOid","HPO_id","HPO_label")]) %>%
# merge(parents, by.x = "HPO_label", by.y = "HPOterm") %>%
merge(ancest_df, by="HPOid", all.x = T) %>%
merge(gene_counts[,c("HPO_id","genes")], by="HPO_id")
meta
#> HPO_id HPOid HPO_label
#> 1: HP.0000002 HP:0000002 Abnormality of body height
#> 2: HP.0000003 HP:0000003 Multicystic kidney dysplasia
#> 3: HP.0000006 HP:0000006 Autosomal dominant inheritance
#> 4: HP.0000007 HP:0000007 Autosomal recessive inheritance
#> 5: HP.0000008 HP:0000008 Abnormal morphology of female internal genitalia
#> ---
#> 9591: HP.3000050 HP:3000050 Abnormality of odontoid tissue
#> 9592: HP.3000062 HP:3000062 Abnormal internal carotid artery morphology
#> 9593: HP.3000072 HP:3000072 Abnormal levator palpebrae superioris morphology
#> 9594: HP.3000075 HP:3000075 Abnormal lingual nerve morphology
#> 9595: HP.3000077 HP:3000077 Abnormal mandible condylar process morphology
#> ancestor_id1 ancestor_id2 ancestor_label1
#> 1: HP:0000118 HP:0001507 Phenotypic abnormality
#> 2: HP:0000118 HP:0000119 Phenotypic abnormality
#> 3: HP:0000005 HP:0000006 Mode of inheritance
#> 4: HP:0000005 HP:0000007 Mode of inheritance
#> 5: HP:0000118 HP:0000119 Phenotypic abnormality
#> ---
#> 9591: HP:0000118 HP:0000924 Phenotypic abnormality
#> 9592: HP:0000118 HP:0001626 Phenotypic abnormality
#> 9593: HP:0000118 HP:0000152 Phenotypic abnormality
#> 9594: HP:0000118 HP:0000152 Phenotypic abnormality
#> 9595: HP:0000118 HP:0000152 Phenotypic abnormality
#> ancestor_label2 genes
#> 1: Growth abnormality 1354
#> 2: Abnormality of the genitourinary system 109
#> 3: Autosomal dominant inheritance 1787
#> 4: Autosomal recessive inheritance 2658
#> 5: Abnormality of the genitourinary system 439
#> ---
#> 9591: Abnormality of the skeletal system 177
#> 9592: Abnormality of the cardiovascular system 9
#> 9593: Abnormality of head or neck 2
#> 9594: Abnormality of head or neck 1
#> 9595: Abnormality of head or neck 3
data.table::fwrite(meta %>%dplyr::select(-HPO_id) %>% dplyr::arrange(genes), "DimReduction/HPO_metadata.csv")Tabula Muris
library(EWCE)
#> Warning: replacing previous import 'biomaRt::select' by 'dplyr::select' when
#> loading 'EWCE'
#> Warning: replacing previous import 'dplyr::combine' by 'gridExtra::combine' when
#> loading 'EWCE'
#> Warning: replacing previous import 'Matrix::cov2cor' by 'stats::cov2cor' when
#> loading 'EWCE'
#> Warning: replacing previous import 'SingleCellExperiment::weights' by
#> 'stats::weights' when loading 'EWCE'
#> Warning: replacing previous import 'dplyr::filter' by 'stats::filter' when
#> loading 'EWCE'
#> Warning: replacing previous import 'dplyr::lag' by 'stats::lag' when loading
#> 'EWCE'
#> Warning: replacing previous import 'Matrix::toeplitz' by 'stats::toeplitz' when
#> loading 'EWCE'
#> Warning: replacing previous import 'Matrix::update' by 'stats::update' when
#> loading 'EWCE'
#> Warning: replacing previous import 'Matrix::tail' by 'utils::tail' when loading
#> 'EWCE'
#> Warning: replacing previous import 'Matrix::head' by 'utils::head' when loading
#> 'EWCE'
try({data("CTD_meta")})
# CTD_meta <- readxl::read_excel("~/projects/model_celltype_conservation/CTD_metadata.xlsx")
ctd_tm <- readRDS(url(subset(CTD_meta, dataset=="TabulaMuris")$url)) Binarize matrix
mat_tm <- binarize_ctd(ctd_tm, level=2,
top_quantiles = 2,
# top_genes = 100,
replace_nonzeros = T)
#> [1] "Selected top 2 quantiles per celltype."
#> [1] "Replacing non-zero values with 1."
dim(mat_tm)
#> [1] 15854 120LaManno2020
ctd_lamanno <- readRDS(url(subset(CTD_meta, dataset=="LaManno2020")$url)) Binarize matrix
mat_lamanno <- binarize_ctd(ctd_lamanno, level=4,
top_quantiles = 4,
replace_nonzeros = T)
#> [1] "Selected top 4 quantiles per celltype."
#> [1] "Replacing non-zero values with 1."
dim(mat_lamanno)
#> [1] 16052 528Descartes
ctd_descartes <- readRDS(url(subset(CTD_meta, dataset=="descartes_SampledData")$url)) Binarize matrix
mat_desc <- binarize_ctd(ctd_descartes,
level=2,
top_genes = 100,
replace_nonzeros = T)
#> [1] "Selected top 100 genes per celltype."
#> [1] "Replacing non-zero values with 1."
dim(mat_desc)
#> [1] 56630 172Merge data
Merge CTD
library(Matrix.utils)
mat_ctd <- Matrix.utils::merge.Matrix(mat_tm, mat_lamanno,
by.x = row.names(mat_tm),
by.y = row.names(mat_lamanno),
all.x=F, all.y=F)
# mat_ctd <- mat_tm
meta_ctd <- data.frame(id=colnames(mat_ctd)) %>%
tidyr::separate(col = "id", remove = F, sep = "[.]",
into = c("species","dataset","celltype"),
extra = "merge") %>%
data.table::data.table()Merge the HPO and CTD matrices.
MAT <-
Matrix.utils::merge.Matrix(mat, mat_ctd,
by.x = row.names(mat),
by.y = row.names(mat_ctd),
all.x=F, all.y=F)
dim(MAT)
#> [1] 3958 6811
# MAT[is.na(MAT)] <- 0
# MAT[MAT!=0] <-1
merged_meta <- data.table::data.table(id=colnames(MAT)) %>%
merge(meta, by.x="id", by.y = "HPO_id", all.x = T) %>%
merge(meta_ctd, by="id", all.x = T) %>%
dplyr::mutate(type=factor(ifelse(is.na(HPOid),"celltype","phenotype"),
levels = c("phenotype","celltype"), ordered = T)) %>%
dplyr::mutate(label=ifelse(is.na(HPO_label),celltype,HPO_label),
# genes=ifelse(is.na(genes), n_genes[id], genes),
species=ifelse(type=="phenotype","human",species),
dataset=ifelse(type=="phenotype","HPO",dataset)) Count genes
n_genes <- sort(DelayedArray::colSums(MAT, na.rm = T), decreasing = T)
# hist(n_genes, breaks = 50)
gene_df <- data.frame(genes=n_genes[n_genes>0]) %>%
tibble::rownames_to_column("id") %>%
merge(merged_meta %>%dplyr::select(-genes), by="id") %>%
data.table::data.table()
ggplot(gene_df, aes(fill=dataset, x=genes)) +
geom_histogram(bins = 50) +
facet_grid(facets=dataset~., scales = "free") +
theme_bw()
merged_meta$genes <- n_genes[merged_meta$id]UMAP
Run UMAP
- Computing Hamming distances should be theoretically better for binary data. But sometimes euclidean produces better inter-group mixing.
The python implementation of UMAP has more distance options.
umap_res <- uwot::umap(X = t(as.matrix(MAT)),
n_components = 2,
ret_extra = c("model","nn","fgraph"),
# metric = "hamming",
verbose = T)
#> 01:50:52 UMAP embedding parameters a = 1.896 b = 0.8006
#> 01:50:54 Read 6811 rows and found 3958 numeric columns
#> 01:50:54 Using Annoy for neighbor search, n_neighbors = 15
#> 01:50:54 Building Annoy index with metric = euclidean, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 01:51:00 Writing NN index file to temp file /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpM1lLtl/file12bd7eea43a2
#> 01:51:00 Searching Annoy index using 6 threads, search_k = 1500
#> 01:51:24 Annoy recall = 100%
#> 01:51:24 Commencing smooth kNN distance calibration using 6 threads
#> 01:51:24 572 smooth knn distance failures
#> 01:51:25 Initializing from normalized Laplacian + noise
#> 01:51:25 Commencing optimization for 500 epochs, with 165276 positive edges
#> 01:51:33 Optimization finishedPlot
embed <- umap_res$embedding %>%
`colnames<-`(paste0("UMAP_",1:ncol(umap_res$embedding))) %>%
data.table::data.table() %>%
dplyr::mutate(id=colnames(MAT)) %>%
merge(merged_meta, by="id", all.x = T)
gg_umap <- ggplot(embed, aes(x=UMAP_1, y=UMAP_2,
color=dataset, shape=type,
label=label, size=genes,
ancestor_label1=ancestor_label1,
ancestor_label2=ancestor_label2)) +
geom_point(alpha=.5) +
# scale_color_manual(values = color_dict) +
scale_color_manual(values = unname(pals::alphabet())) +
theme_bw()
plotly::ggplotly(gg_umap)
#> Warning: Using shapes for an ordinal variable is not advisedCluster
library(Seurat)
#> Registered S3 method overwritten by 'spatstat':
#> method from
#> print.boxx cli
#> Attaching SeuratObject
seurat <- Seurat::CreateSeuratObject(counts = MAT,
meta.data = data.frame(embed, row.names = embed$id))
#### Select variable features ####
# Better to include more (perhaps all?) genes
seurat <- Seurat::FindVariableFeatures(seurat)
# nfeatures=nrow(seurat))
gg_var <- Seurat::VariableFeaturePlot(seurat)
# Seurat::VariableFeatures(seurat) <- row.names(seurat)
seurat <- Seurat::NormalizeData(seurat)
seurat <- Seurat::ScaleData(seurat, vars.to.regress = c("type","dataset")) # genes
#> Regressing out type, dataset
#> Centering and scaling data matrix
seurat <- Seurat::RunPCA(seurat) # slot="count"
#> PC_ 1
#> Positive: ASPSCR1, ANXA5, ADD1, ANO6, CYCS, CYP2C9, CYP2A6, IFNAR2, MC3R, KCNE3
#> ZNF644, CABP2, WBP2, GREM1, KCNN4, CNGB3, MSH3, CLCN5, IL6R, LCK
#> CACNA1S, CYP1B1, CDKN1A, DAXX, CDKN2C, CDKN1B, BLK, CD3E, ANGPTL6, CD3D
#> Negative: RERE, TRRAP, HUWE1, SETD5, HDAC8, NSUN2, KLHL7, SKI, SMC3, RPL10
#> RAB18, STAG1, SMC1A, ADAT3, RAD21, AUTS2, NIPBL, ARL3, RAB3GAP1, KCNAB2
#> TCF20, ZMIZ1, PRDM16, DHDDS, MED25, RLIM, MAPK8IP3, EXOSC9, RAB3GAP2, TRAPPC4
#> PC_ 2
#> Positive: NXN, ROR2, WNT5A, DVL3, FANCL, FZD2, DVL1, UBE2T, CHST3, FLNB
#> FANCF, MAD2L2, FANCE, SLX4, FANCB, FANCG, RAD51C, BRIP1, FANCC, B3GAT3
#> FANCA, DHODH, SF3B4, FANCI, WNT7A, MEGF8, RPL26, RFWD3, WDR35, CUL7
#> Negative: UBA5, GABRA5, NUS1, GRIN2D, YWHAG, NECAP1, FGF12, EEF1A2, TRAK1, AP3B2
#> ZC3H14, TUSC3, MBOAT7, SCN3A, ST3GAL3, NDUFA9, GABRB2, NTRK2, KCNB1, NDUFV1
#> CACNA1B, CYFIP2, CLIP1, GABRA2, NDUFV2, SLC1A2, NDUFS8, KCNA2, NDUFS4, NDUFS7
#> PC_ 3
#> Positive: CD81, ICOS, CTLA4, CR2, TNFRSF13C, CD19, NFKB1, FAS, IL12A, TNFRSF13B
#> MEFV, PRKCD, WAS, TCF3, MYD88, PSMB4, RAG1, NPM1, STAT4, RAG2
#> IRF2BP2, ZAP70, LRBA, CD79A, IKZF1, DNASE1L3, IL23R, IL7R, RASGRP1, LRRC8A
#> Negative: MBD5, PPP3CA, EEF1A2, SET, KDM5B, MAN1B1, MYT1L, HIVEP2, DCPS, GRIN2B
#> MED12L, CAMK2A, CIC, MAPK8IP3, TAOK1, RAB11A, DYNC1H1, ZC3H14, TUSC3, ACTL6B
#> STXBP1, CLIP1, CDH15, TRAPPC9, MBOAT7, GRIN1, UPF3B, CACNG2, SLC45A1, BRSK2
#> PC_ 4
#> Positive: TOPORS, PRPF6, RP9, PRPF31, PDE6A, PDE6G, RP1L1, FSCN2, MAK, PRPF3
#> FAM161A, SPATA7, ZNF408, SLC7A14, IDH3A, AHR, MERTK, CRX, REEP6, NR2E3
#> ROM1, CLRN1, CA4, PDE6B, IMPDH1, USH2A, IMPG2, KIAA1549, RHO, DHX38
#> Negative: ICOS, TNFRSF13C, CR2, CD19, NFKB1, PSMB4, TCF3, TNFRSF13B, CD81, RAG1
#> RAG2, IL7R, ZAP70, CD79A, LRRC8A, PSMB9, IL2RG, CD79B, ADA, MYPN
#> CIITA, WDR1, CD247, CD3D, ELANE, CD3E, TPM3, CFL2, LRBA, KBTBD13
#> PC_ 5
#> Positive: TPM3, ACTA1, NEB, KBTBD13, CFL2, GFPT1, AGRN, TTN, LMOD3, COL13A1
#> BIN1, MYO9A, ITGA7, CHAT, MYOT, SLC5A7, CHRNE, DOK7, SLC18A3, KY
#> SYT2, SNAP25, COL6A1, MYPN, CHRNB1, ANO5, COL6A2, COL12A1, DNM2, RYR1
#> Negative: KDM5B, DOCK8, GRIN2B, TAOK1, RAB11A, CIC, HIVEP2, ZC3H14, CDH15, TUSC3
#> BRSK2, CACNG2, CLIP1, TRPM3, KCNQ5, SET, KIRREL3, CAMK2A, TNIK, ST3GAL3
#> CRADD, EEF1A2, MBOAT7, SLC45A1, GRIN1, NDST1, FMN2, MED12L, DCPS, HNMT
# Seurat::ElbowPlot(seurat, ndims = 50)
seurat <- Seurat::FindNeighbors(seurat)
#> Computing nearest neighbor graph
#> Computing SNN
seurat <- Seurat::RunUMAP(seurat, dims=1:50) #metric="hamming")
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session
#> 01:51:57 UMAP embedding parameters a = 0.9922 b = 1.112
#> 01:51:57 Read 6811 rows and found 50 numeric columns
#> 01:51:57 Using Annoy for neighbor search, n_neighbors = 30
#> 01:51:57 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 01:51:59 Writing NN index file to temp file /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpM1lLtl/file12bd775acd5d4
#> 01:51:59 Searching Annoy index using 1 thread, search_k = 3000
#> 01:52:01 Annoy recall = 99.93%
#> 01:52:01 Commencing smooth kNN distance calibration using 1 thread
#> 01:52:02 151 smooth knn distance failures
#> 01:52:03 Initializing from normalized Laplacian + noise
#> 01:52:03 Commencing optimization for 500 epochs, with 289176 positive edges
#> 01:52:13 Optimization finished
seurat <- Seurat::FindClusters(seurat, reduction="umap")
#> Warning: The following arguments are not used: reduction
#> Warning: The following arguments are not used: reduction
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 6811
#> Number of edges: 193952
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8689
#> Number of communities: 25
#> Elapsed time: 0 seconds
Seurat::DimPlot(seurat, group.by = c("seurat_clusters","type"))
Term enrichment
TF-IDF
seurat <- seurat_tfidf(seurat,
label_var = "label",
cluster_var = "seurat_clusters",
force_new = T)
#> Joining, by = "word"
#> Joining, by = "cluster"
#> Joining, by = "cluster"
saveRDS(seurat, "DimReduction/seurat.rds")word2vec
Followstutorial here.
library(word2vec)
model <- word2vec(x = setNames(seurat@meta.data$id,
seurat@meta.data$seurat_clusters),
dim =15,iter =20)
embedding <-as.matrix(model)
library(uwot)
viz <- uwot::umap(embedding,n_neighbors =15,n_threads =10)
rownames(viz) <-rownames(embedding)
df <- viz %>% `colnames<-`(c("x","y")) %>% data.frame() %>%tibble::rownames_to_column("word")
ggplot(df,aes(x =x,y =y,label =word)) +
geom_point() +
ggrepel::geom_text_repel() +
theme_void() +
labs(title ="word2vec")seurat$ancestor_label1[seurat$type=="celltype"] <- "Cell-type"
seurat$ancestor_label1[is.na(seurat$ancestor_label1)] <- "Other phenotype"
gg_tfidf <- umap_tfidf(seurat = seurat,
size_var = "genes",
shape="type",
# color_var = "dataset",
color_var="ancestor_label1",
dataset="dataset",
shape_var="type",
species="species",
Label="label",
ancestor_label1="ancestor_label1",
density_palette = "Blues",
show_plot=T)
#> Joining, by = "word"
#> Joining, by = "cluster"
#> Joining, by = "cluster"
#> `summarise()` has grouped output by 'cluster'. You can override using the `.groups` argument.
#> Warning: Ignoring unknown aesthetics: dataset, shape_var, species, Label,
#> ancestor_label1
#> Warning: Using shapes for an ordinal variable is not advised
plotly::ggplotly(gg_tfidf)
#> Warning: Using shapes for an ordinal variable is not advised
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issues
#> Warning in geom2trace.default(dots[[1L]][[25L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomLabelRepel() has yet to be implemented in plotly.
#> If you'd like to see this geom implemented,
#> Please open an issue with your example code at
#> https://github.com/ropensci/plotly/issuesSession info
utils::sessionInfo()
#> R version 4.0.4 (2021-02-15)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] tidytext_0.3.0 SeuratObject_4.0.0 Seurat_4.0.0 Matrix.utils_0.9.8
#> [5] EWCE_0.99.2 ontologyIndex_2.7 Matrix_1.3-2 ggplot2_3.3.3
#> [9] dplyr_1.0.5
#>
#> loaded via a namespace (and not attached):
#> [1] utf8_1.2.1 reticulate_1.18
#> [3] tidyselect_1.1.0 RSQLite_2.2.4
#> [5] AnnotationDbi_1.52.0 htmlwidgets_1.5.3
#> [7] grid_4.0.4 Rtsne_0.15
#> [9] munsell_0.5.0 codetools_0.2-18
#> [11] ica_1.0-2 future_1.21.0
#> [13] miniUI_0.1.1.1 withr_2.4.1
#> [15] colorspace_2.0-0 Biobase_2.50.0
#> [17] highr_0.8 knitr_1.31
#> [19] rstudioapi_0.13 stats4_4.0.4
#> [21] SingleCellExperiment_1.12.0 ROCR_1.0-11
#> [23] tensor_1.5 listenv_0.8.0
#> [25] MatrixGenerics_1.2.1 labeling_0.4.2
#> [27] GenomeInfoDbData_1.2.4 polyclip_1.10-0
#> [29] bit64_4.0.5 farver_2.1.0
#> [31] rprojroot_2.0.2 parallelly_1.23.0
#> [33] vctrs_0.3.6 generics_0.1.0
#> [35] xfun_0.22 BiocFileCache_1.14.0
#> [37] R6_2.5.0 GenomeInfoDb_1.26.4
#> [39] isoband_0.2.4 pals_1.6
#> [41] bitops_1.0-6 spatstat.utils_2.0-0
#> [43] cachem_1.0.4 DelayedArray_0.16.3
#> [45] assertthat_0.2.1 promises_1.2.0.1
#> [47] scales_1.1.1 gtable_0.3.0
#> [49] globals_0.14.0 goftest_1.2-2
#> [51] rlang_0.4.10 splines_4.0.4
#> [53] lazyeval_0.2.2 dichromat_2.0-0
#> [55] yaml_2.2.1 reshape2_1.4.4
#> [57] abind_1.4-5 crosstalk_1.1.1
#> [59] httpuv_1.5.5 tokenizers_0.2.1
#> [61] tools_4.0.4 ellipsis_0.3.1
#> [63] jquerylib_0.1.3 RColorBrewer_1.1-2
#> [65] ggdendro_0.1.22 BiocGenerics_0.36.0
#> [67] ggridges_0.5.3 Rcpp_1.0.6
#> [69] plyr_1.8.6 progress_1.2.2
#> [71] zlibbioc_1.36.0 purrr_0.3.4
#> [73] RCurl_1.98-1.3 prettyunits_1.1.1
#> [75] rpart_4.1-15 openssl_1.4.3
#> [77] deldir_0.2-10 pbapply_1.4-3
#> [79] cowplot_1.1.1 S4Vectors_0.28.1
#> [81] zoo_1.8-9 SummarizedExperiment_1.20.0
#> [83] grr_0.9.5 ggrepel_0.9.1
#> [85] cluster_2.1.1 here_1.0.1
#> [87] magrittr_2.0.1 data.table_1.13.6
#> [89] RSpectra_0.16-0 scattermore_0.7
#> [91] lmtest_0.9-38 RANN_2.6.1
#> [93] SnowballC_0.7.0 fitdistrplus_1.1-3
#> [95] matrixStats_0.58.0 hms_1.0.0
#> [97] patchwork_1.1.1 mime_0.10
#> [99] evaluate_0.14 xtable_1.8-4
#> [101] XML_3.99-0.6 IRanges_2.24.1
#> [103] gridExtra_2.3 compiler_4.0.4
#> [105] biomaRt_2.46.3 tibble_3.1.0
#> [107] maps_3.3.0 KernSmooth_2.23-18
#> [109] crayon_1.4.1 htmltools_0.5.1.1
#> [111] mgcv_1.8-34 later_1.1.0.1
#> [113] tidyr_1.1.3 DBI_1.1.1
#> [115] dbplyr_2.1.0 MASS_7.3-53.1
#> [117] rappdirs_0.3.3 cli_2.3.1
#> [119] parallel_4.0.4 igraph_1.2.6
#> [121] GenomicRanges_1.42.0 pkgconfig_2.0.3
#> [123] plotly_4.9.3 xml2_1.3.2
#> [125] bslib_0.2.4 XVector_0.30.0
#> [127] janeaustenr_0.1.5 stringr_1.4.0
#> [129] digest_0.6.27 sctransform_0.3.2
#> [131] RcppAnnoy_0.0.18 spatstat.data_2.0-0
#> [133] rmarkdown_2.7 HGNChelper_0.8.1
#> [135] leiden_0.3.7 uwot_0.1.10
#> [137] curl_4.3 shiny_1.6.0
#> [139] lifecycle_1.0.0 nlme_3.1-152
#> [141] jsonlite_1.7.2 mapproj_1.2.7
#> [143] viridisLite_0.3.0 askpass_1.1
#> [145] limma_3.46.0 fansi_0.4.2
#> [147] pillar_1.5.1 lattice_0.20-41
#> [149] homologene_1.4.68.19.3.27 fastmap_1.1.0
#> [151] httr_1.4.2 survival_3.2-7
#> [153] glue_1.4.2 RNOmni_1.0.0
#> [155] spatstat_1.64-1 png_0.1-7
#> [157] bit_4.0.4 stringi_1.5.3
#> [159] sass_0.3.1 blob_1.2.1
#> [161] memoise_2.0.0 irlba_2.3.3
#> [163] future.apply_1.7.0